Probabilistic group recommendation via information matching

Jagadeesh Gorla, Neal Lathia, Stephen Robertson, Jun Wang
2013 Proceedings of the 22nd international conference on World Wide Web - WWW '13  
Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for example, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members' ratings into a single profile, or computing ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the
more » ... references that arise in individuals when they are members of a group. In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item's relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group recommendation strategy in order to rank items for a set of individuals. We evaluate the model's efficacy at generating recommendations for both single individuals and groups using the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics.
doi:10.1145/2488388.2488432 dblp:conf/www/GorlaLR013 fatcat:hts4vsvhr5dixioy4rggrxmb3u